A study to assess the value of post-stack seismic amplitude data in forecasting fluid production from a Gulf-of-Mexico reservoir

Gambús-Ordaz, Maika; Torres‐Verdín, Carlos

doi:10.1016/j.petrol.2008.06.001

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Formulations similar to the rock physics geo statistical inversion, and additional example ap plications, can be found in GrijalbaCuenca et al (2000), Eide et al (2002), Contreras and Torres Verdín (2005), GambúsOrdaz and TorresVerdín (2008), and Merletti and TorresVerdín. (2010), among others.…”

Section: Rock Physics Geostatistical Inversionmentioning

confidence: 64%

“…These PDFs are modeled from well logs at specific target stra ta. In GambúsOrdaz and TorresVerdín (2008), the geostatistical inversion procedure is used to Downloaded 07/08/17 to 186.88.151.214. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/ Seismic, rock physics, spatial models, and their integration in reservoir geophysics M1-43 Downloaded 07/08/17 to 186.88.151.214.…”

Section: Rock Physics Geostatistical Inversionmentioning

confidence: 99%

See 1 more Smart Citation

7. Seismic, rock physics, spatial models, and their integration in reservoir geophysics

Bosch

Mukerji²,

González

2014

Encyclopedia of Exploration Geophysics

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seismic inversion and rock physics models. Furthermore well logs allow for local validation of the estimations and constraint for the struc ture and properties. Combining the spatially dis tributed information provided by seismic surveys with the localized information obtained from well logging tools requires handling disparate property support scales and use of spatial statisti cal models. Most common models are based on the spatial homogeneity of the mean (one point statistics) and the spatial covariance (two point statistics). Cokriging estimation and multivariate Gaussian simulation are examples of these meth ods, which are very useful in the spatial combina tion of information. In addition, methods based on multipoint statistics are used to model com plex morphology. The integration of the overall data, information, geophysical, and geological knowledge into a reliable reservoir model is elab orated via diverse workflows. A basic sequence includes seismic interpretation, seismic inver sion, reservoir characterization, geostatistical es timation, geostatistical simulation, and dynamic fluid flow modeling. It is common to cycle within the sequence to update the model as additional information about the reservoir arrives. Some workflows proceed in a stepwise manner, while others, with increasing computational require ments, employ joint inversion formulations where multiproperty models are estimated to jointly satisfy the complete set of available data.

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